Overview

Brought to you by YData

Dataset statistics

Number of variables	25
Number of observations	10226
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	2.0 MiB
Average record size in memory	200.0 B

Variable types

Categorical	14
Text	11

Reproduction

Analysis started	2024-11-05 02:03:07.538188
Analysis finished	2024-11-05 02:03:16.162053
Duration	8.62 seconds
Software version	ydata-profiling vv4.12.0
Download configuration	config.json

What is your age (# years)?
Categorical

Distinct	11
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

25-29	2523
30-34	2063
35-39	1420
22-24	1306
40-44	969
Other values (6)	1945

Length

Max length	5
Median length	5
Mean length	4.9906122
Min length	3

Characters and Unicode

Total characters	51034
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	22-24
2nd row	40-44
3rd row	22-24
4th row	50-54
5th row	22-24

Common Values

Value	Count	Frequency (%)
25-29	2523	24.7%
30-34	2063	20.2%
35-39	1420	13.9%
22-24	1306	12.8%
40-44	969	9.5%
45-49	642	6.3%
50-54	464	4.5%
18-21	317	3.1%
55-59	264	2.6%
60-69	210	2.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
25-29	2523	24.7%
30-34	2063	20.2%
35-39	1420	13.9%
22-24	1306	12.8%
40-44	969	9.5%
45-49	642	6.3%
50-54	464	4.5%
18-21	317	3.1%
55-59	264	2.6%
60-69	210	2.1%

Most occurring characters

Value	Count	Frequency (%)
-	10178	19.9%
2	9281	18.2%
4	8024	15.7%
3	6966	13.6%
5	6305	12.4%
9	5059	9.9%
0	3754	7.4%
1	634	1.2%
6	420	0.8%
8	317	0.6%
Other values (2)	96	0.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	51034	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
-	10178	19.9%
2	9281	18.2%
4	8024	15.7%
3	6966	13.6%
5	6305	12.4%
9	5059	9.9%
0	3754	7.4%
1	634	1.2%
6	420	0.8%
8	317	0.6%
Other values (2)	96	0.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	51034	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
-	10178	19.9%
2	9281	18.2%
4	8024	15.7%
3	6966	13.6%
5	6305	12.4%
9	5059	9.9%
0	3754	7.4%
1	634	1.2%
6	420	0.8%
8	317	0.6%
Other values (2)	96	0.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	51034	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
-	10178	19.9%
2	9281	18.2%
4	8024	15.7%
3	6966	13.6%
5	6305	12.4%
9	5059	9.9%
0	3754	7.4%
1	634	1.2%
6	420	0.8%
8	317	0.6%
Other values (2)	96	0.2%

What is your gender?
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Male	8799
Female	1427

Length

Max length	6
Median length	4
Mean length	4.2790925
Min length	4

Characters and Unicode

Total characters	43758
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Male
2nd row	Male
3rd row	Male
4th row	Male
5th row	Male

Common Values

Value	Count	Frequency (%)
Male	8799	86.0%
Female	1427	14.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
male	8799	86.0%
female	1427	14.0%

Most occurring characters

Value	Count	Frequency (%)
e	11653	26.6%
a	10226	23.4%
l	10226	23.4%
M	8799	20.1%
F	1427	3.3%
m	1427	3.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	43758	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	11653	26.6%
a	10226	23.4%
l	10226	23.4%
M	8799	20.1%
F	1427	3.3%
m	1427	3.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	43758	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	11653	26.6%
a	10226	23.4%
l	10226	23.4%
M	8799	20.1%
F	1427	3.3%
m	1427	3.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	43758	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	11653	26.6%
a	10226	23.4%
l	10226	23.4%
M	8799	20.1%
F	1427	3.3%
m	1427	3.3%

In which country do you currently reside?
Text

Distinct	59
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	52
Median length	28
Mean length	10.89556
Min length	4

Characters and Unicode

Total characters	111418
Distinct characters	49
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	France
2nd row	Australia
3rd row	India
4th row	France
5th row	India

Value	Count	Frequency (%)
of	2202	12.0%
united	2122	11.6%
india	1879	10.2%
states	1838	10.0%
america	1838	10.0%
other	529	2.9%
brazil	456	2.5%
japan	402	2.2%
russia	362	2.0%
ireland	320	1.7%
Other values (63)	6391	34.8%

Most occurring characters

Value	Count	Frequency (%)
a	13319	12.0%
i	10131	9.1%
e	9929	8.9%
n	8713	7.8%
t	8232	7.4%
	8113	7.3%
r	6512	5.8%
d	5732	5.1%
o	4093	3.7%
s	3341	3.0%
Other values (39)	33303	29.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	111418	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	13319	12.0%
i	10131	9.1%
e	9929	8.9%
n	8713	7.8%
t	8232	7.4%
	8113	7.3%
r	6512	5.8%
d	5732	5.1%
o	4093	3.7%
s	3341	3.0%
Other values (39)	33303	29.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	111418	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	13319	12.0%
i	10131	9.1%
e	9929	8.9%
n	8713	7.8%
t	8232	7.4%
	8113	7.3%
r	6512	5.8%
d	5732	5.1%
o	4093	3.7%
s	3341	3.0%
Other values (39)	33303	29.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	111418	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	13319	12.0%
i	10131	9.1%
e	9929	8.9%
n	8713	7.8%
t	8232	7.4%
	8113	7.3%
r	6512	5.8%
d	5732	5.1%
o	4093	3.7%
s	3341	3.0%
Other values (39)	33303	29.9%

What is the highest level of formal education that you have attained or plan to attain within the next 2 years?
Categorical

Distinct	7
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Master’s degree	4882
Bachelor’s degree	2680
Doctoral degree	1797
Professional degree	353
Some college/university study without earning a bachelor’s degree	308
Other values (2)	206

Length

Max length	65
Median length	15
Mean length	17.436534
Min length	15

Characters and Unicode

Total characters	178306
Distinct characters	31
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Master’s degree
2nd row	Master’s degree
3rd row	Bachelor’s degree
4th row	Master’s degree
5th row	Master’s degree

Common Values

Value	Count	Frequency (%)
Master’s degree	4882	47.7%
Bachelor’s degree	2680	26.2%
Doctoral degree	1797	17.6%
Professional degree	353	3.5%
Some college/university study without earning a bachelor’s degree	308	3.0%
I prefer not to answer	113	1.1%
No formal education past high school	93	0.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
degree	10020	43.5%
master’s	4882	21.2%
bachelor’s	2988	13.0%
doctoral	1797	7.8%
professional	353	1.5%
some	308	1.3%
college/university	308	1.3%
study	308	1.3%
without	308	1.3%
earning	308	1.3%
Other values (12)	1431	6.2%

Most occurring characters

Value	Count	Frequency (%)
e	40255	22.6%
r	21088	11.8%
s	14373	8.1%
	12785	7.2%
a	11028	6.2%
g	10729	6.0%
d	10421	5.8%
o	8903	5.0%
t	8323	4.7%
’	7870	4.4%
Other values (21)	32531	18.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	178306	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	40255	22.6%
r	21088	11.8%
s	14373	8.1%
	12785	7.2%
a	11028	6.2%
g	10729	6.0%
d	10421	5.8%
o	8903	5.0%
t	8323	4.7%
’	7870	4.4%
Other values (21)	32531	18.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	178306	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	40255	22.6%
r	21088	11.8%
s	14373	8.1%
	12785	7.2%
a	11028	6.2%
g	10729	6.0%
d	10421	5.8%
o	8903	5.0%
t	8323	4.7%
’	7870	4.4%
Other values (21)	32531	18.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	178306	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	40255	22.6%
r	21088	11.8%
s	14373	8.1%
	12785	7.2%
a	11028	6.2%
g	10729	6.0%
d	10421	5.8%
o	8903	5.0%
t	8323	4.7%
’	7870	4.4%
Other values (21)	32531	18.2%

Select the title most similar to your current role (or most recent title if retired)
Categorical

Distinct	10
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Data Scientist	3243
Software Engineer	1842
Data Analyst	1153
Other	1118
Research Scientist	1071
Other values (5)	1799

Length

Max length	23
Median length	18
Mean length	14.306963
Min length	5

Characters and Unicode

Total characters	146303
Distinct characters	30
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Software Engineer
2nd row	Other
3rd row	Other
4th row	Data Scientist
5th row	Data Scientist

Common Values

Value	Count	Frequency (%)
Data Scientist	3243	31.7%
Software Engineer	1842	18.0%
Data Analyst	1153	11.3%
Other	1118	10.9%
Research Scientist	1071	10.5%
Product/Project Manager	530	5.2%
Business Analyst	509	5.0%
Data Engineer	448	4.4%
Statistician	203	2.0%
DBA/Database Engineer	109	1.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
data	4844	25.3%
scientist	4314	22.5%
engineer	2399	12.5%
software	1842	9.6%
analyst	1662	8.7%
other	1118	5.8%
research	1071	5.6%
product/project	530	2.8%
manager	530	2.8%
business	509	2.7%
Other values (2)	312	1.6%

Most occurring characters

Value	Count	Frequency (%)
t	19872	13.6%
a	16056	11.0%
e	15892	10.9%
i	12145	8.3%
n	12016	8.2%
	8905	6.1%
s	8886	6.1%
r	8020	5.5%
c	6648	4.5%
S	6359	4.3%
Other values (20)	31504	21.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	146303	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
t	19872	13.6%
a	16056	11.0%
e	15892	10.9%
i	12145	8.3%
n	12016	8.2%
	8905	6.1%
s	8886	6.1%
r	8020	5.5%
c	6648	4.5%
S	6359	4.3%
Other values (20)	31504	21.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	146303	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
t	19872	13.6%
a	16056	11.0%
e	15892	10.9%
i	12145	8.3%
n	12016	8.2%
	8905	6.1%
s	8886	6.1%
r	8020	5.5%
c	6648	4.5%
S	6359	4.3%
Other values (20)	31504	21.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	146303	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
t	19872	13.6%
a	16056	11.0%
e	15892	10.9%
i	12145	8.3%
n	12016	8.2%
	8905	6.1%
s	8886	6.1%
r	8020	5.5%
c	6648	4.5%
S	6359	4.3%
Other values (20)	31504	21.5%

What is the size of the company where you are employed?
Categorical

Distinct	5
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

0-49 employees	2849
> 10,000 employees	2327
1000-9,999 employees	2009
50-249 employees	1687
250-999 employees	1354

Length

Max length	20
Median length	18
Mean length	16.816155
Min length	14

Characters and Unicode

Total characters	171962
Distinct characters	17
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1000-9,999 employees
2nd row	> 10,000 employees
3rd row	0-49 employees
4th row	0-49 employees
5th row	50-249 employees

Common Values

Value	Count	Frequency (%)
0-49 employees	2849	27.9%
> 10,000 employees	2327	22.8%
1000-9,999 employees	2009	19.6%
50-249 employees	1687	16.5%
250-999 employees	1354	13.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
employees	10226	44.9%
0-49	2849	12.5%
	2327	10.2%
10,000	2327	10.2%
1000-9,999	2009	8.8%
50-249	1687	7.4%
250-999	1354	5.9%

Most occurring characters

Value	Count	Frequency (%)
e	30678	17.8%
0	21225	12.3%
9	16634	9.7%
	12553	7.3%
o	10226	5.9%
s	10226	5.9%
y	10226	5.9%
l	10226	5.9%
p	10226	5.9%
m	10226	5.9%
Other values (7)	29516	17.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	171962	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	30678	17.8%
0	21225	12.3%
9	16634	9.7%
	12553	7.3%
o	10226	5.9%
s	10226	5.9%
y	10226	5.9%
l	10226	5.9%
p	10226	5.9%
m	10226	5.9%
Other values (7)	29516	17.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	171962	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	30678	17.8%
0	21225	12.3%
9	16634	9.7%
	12553	7.3%
o	10226	5.9%
s	10226	5.9%
y	10226	5.9%
l	10226	5.9%
p	10226	5.9%
m	10226	5.9%
Other values (7)	29516	17.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	171962	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	30678	17.8%
0	21225	12.3%
9	16634	9.7%
	12553	7.3%
o	10226	5.9%
s	10226	5.9%
y	10226	5.9%
l	10226	5.9%
p	10226	5.9%
m	10226	5.9%
Other values (7)	29516	17.2%

Approximately how many individuals are responsible for data science workloads at your place of business?
Categorical

Distinct	7
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

20+	2415
1-2	2306
3-4	1792
5-9	1421
0	1232
Other values (2)	1060

Length

Max length	5
Median length	3
Mean length	2.9663603
Min length	1

Characters and Unicode

Total characters	30334
Distinct characters	9
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	20+
3rd row	0
4th row	3-4
5th row	20+

Common Values

Value	Count	Frequency (%)
20+	2415	23.6%
1-2	2306	22.6%
3-4	1792	17.5%
5-9	1421	13.9%
0	1232	12.0%
10-14	738	7.2%
15-19	322	3.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
20	2415	23.6%
1-2	2306	22.6%
3-4	1792	17.5%
5-9	1421	13.9%
0	1232	12.0%
10-14	738	7.2%
15-19	322	3.1%

Most occurring characters

Value	Count	Frequency (%)
-	6579	21.7%
2	4721	15.6%
1	4426	14.6%
0	4385	14.5%
4	2530	8.3%
+	2415	8.0%
3	1792	5.9%
5	1743	5.7%
9	1743	5.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	30334	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
-	6579	21.7%
2	4721	15.6%
1	4426	14.6%
0	4385	14.5%
4	2530	8.3%
+	2415	8.0%
3	1792	5.9%
5	1743	5.7%
9	1743	5.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	30334	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
-	6579	21.7%
2	4721	15.6%
1	4426	14.6%
0	4385	14.5%
4	2530	8.3%
+	2415	8.0%
3	1792	5.9%
5	1743	5.7%
9	1743	5.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	30334	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
-	6579	21.7%
2	4721	15.6%
1	4426	14.6%
0	4385	14.5%
4	2530	8.3%
+	2415	8.0%
3	1792	5.9%
5	1743	5.7%
9	1743	5.7%

Does your current employer incorporate machine learning methods into their business?
Categorical

Distinct	6
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

We recently started using ML methods (i.e., models in production for less than 2 years)	2236
We are exploring ML methods (and may one day put a model into production)	2195
We have well established ML methods (i.e., models in production for more than 2 years)	2077
No (we do not use ML methods)	1737
We use ML methods for generating insights (but do not put working models into production)	1246

Length

Max length	89
Median length	86
Mean length	68.864757
Min length	13

Characters and Unicode

Total characters	704211
Distinct characters	34
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	I do not know
2nd row	I do not know
3rd row	No (we do not use ML methods)
4th row	We have well established ML methods (i.e., models in production for more than 2 years)
5th row	We are exploring ML methods (and may one day put a model into production)

Common Values

Value	Count	Frequency (%)
We recently started using ML methods (i.e., models in production for less than 2 years)	2236	21.9%
We are exploring ML methods (and may one day put a model into production)	2195	21.5%
We have well established ML methods (i.e., models in production for more than 2 years)	2077	20.3%
No (we do not use ML methods)	1737	17.0%
We use ML methods for generating insights (but do not put working models into production)	1246	12.2%
I do not know	735	7.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
we	9491	7.3%
ml	9491	7.3%
methods	9491	7.3%
production	7754	6.0%
models	5559	4.3%
for	5559	4.3%
years	4313	3.3%
i.e	4313	3.3%
in	4313	3.3%
than	4313	3.3%
Other values (29)	64617	50.0%

Most occurring characters

Value	Count	Frequency (%)
	118988	16.9%
e	66751	9.5%
o	59374	8.4%
t	44681	6.3%
n	40315	5.7%
s	37936	5.4%
d	37420	5.3%
i	31313	4.4%
r	31057	4.4%
a	27237	3.9%
Other values (24)	209139	29.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	704211	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	118988	16.9%
e	66751	9.5%
o	59374	8.4%
t	44681	6.3%
n	40315	5.7%
s	37936	5.4%
d	37420	5.3%
i	31313	4.4%
r	31057	4.4%
a	27237	3.9%
Other values (24)	209139	29.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	704211	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	118988	16.9%
e	66751	9.5%
o	59374	8.4%
t	44681	6.3%
n	40315	5.7%
s	37936	5.4%
d	37420	5.3%
i	31313	4.4%
r	31057	4.4%
a	27237	3.9%
Other values (24)	209139	29.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	704211	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	118988	16.9%
e	66751	9.5%
o	59374	8.4%
t	44681	6.3%
n	40315	5.7%
s	37936	5.4%
d	37420	5.3%
i	31313	4.4%
r	31057	4.4%
a	27237	3.9%
Other values (24)	209139	29.7%

What is your current yearly compensation (approximate $USD)?
Categorical

Distinct	25
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

$0-999	1064
10,000-14,999	685
100,000-124,999	649
30,000-39,999	633
40,000-49,999	622
Other values (20)	6573

Length

Max length	15
Median length	13
Mean length	12.214942
Min length	6

Characters and Unicode

Total characters	124910
Distinct characters	15
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	30,000-39,999
2nd row	250,000-299,999
3rd row	4,000-4,999
4th row	60,000-69,999
5th row	10,000-14,999

Common Values

Value	Count	Frequency (%)
$0-999	1064	10.4%
10,000-14,999	685	6.7%
100,000-124,999	649	6.3%
30,000-39,999	633	6.2%
40,000-49,999	622	6.1%
50,000-59,999	603	5.9%
60,000-69,999	501	4.9%
70,000-79,999	458	4.5%
15,000-19,999	452	4.4%
20,000-24,999	438	4.3%
Other values (15)	4121	40.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
0-999	1064	10.4%
10,000-14,999	685	6.7%
100,000-124,999	649	6.3%
30,000-39,999	633	6.2%
40,000-49,999	622	6.1%
50,000-59,999	603	5.9%
60,000-69,999	501	4.9%
70,000-79,999	458	4.5%
15,000-19,999	452	4.4%
20,000-24,999	438	4.3%
Other values (16)	4169	40.6%

Most occurring characters

Value	Count	Frequency (%)
9	36684	29.4%
0	35355	28.3%
,	18276	14.6%
-	10178	8.1%
1	6036	4.8%
4	4480	3.6%
5	3783	3.0%
2	3758	3.0%
3	1796	1.4%
7	1656	1.3%
Other values (5)	2908	2.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	124910	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
9	36684	29.4%
0	35355	28.3%
,	18276	14.6%
-	10178	8.1%
1	6036	4.8%
4	4480	3.6%
5	3783	3.0%
2	3758	3.0%
3	1796	1.4%
7	1656	1.3%
Other values (5)	2908	2.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	124910	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
9	36684	29.4%
0	35355	28.3%
,	18276	14.6%
-	10178	8.1%
1	6036	4.8%
4	4480	3.6%
5	3783	3.0%
2	3758	3.0%
3	1796	1.4%
7	1656	1.3%
Other values (5)	2908	2.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	124910	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
9	36684	29.4%
0	35355	28.3%
,	18276	14.6%
-	10178	8.1%
1	6036	4.8%
4	4480	3.6%
5	3783	3.0%
2	3758	3.0%
3	1796	1.4%
7	1656	1.3%
Other values (5)	2908	2.3%

Approximately how much money have you spent on machine learning and/or cloud computing products at your work in the past 5 years?
Categorical

Distinct	6
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

$0 (USD)	3160
$100-$999	1995
$1000-$9,999	1859
$1-$99	1215
$10,000-$99,999	1128

Length

Max length	17
Median length	15
Mean length	10.221592
Min length	6

Characters and Unicode

Total characters	104526
Distinct characters	13
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	$0 (USD)
2nd row	$10,000-$99,999
3rd row	$0 (USD)
4th row	$10,000-$99,999
5th row	$100-$999

Common Values

Value	Count	Frequency (%)
$0 (USD)	3160	30.9%
$100-$999	1995	19.5%
$1000-$9,999	1859	18.2%
$1-$99	1215	11.9%
$10,000-$99,999	1128	11.0%
> $100,000 ($USD)	869	8.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
usd	4029	26.6%
0	3160	20.9%
100-$999	1995	13.2%
1000-$9,999	1859	12.3%
1-$99	1215	8.0%
10,000-$99,999	1128	7.5%
	869	5.7%
100,000	869	5.7%

Most occurring characters

Value	Count	Frequency (%)
0	21584	20.6%
9	21491	20.6%
$	17292	16.5%
1	7066	6.8%
-	6197	5.9%
,	4984	4.8%
	4898	4.7%
(	4029	3.9%
U	4029	3.9%
S	4029	3.9%
Other values (3)	8927	8.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	104526	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	21584	20.6%
9	21491	20.6%
$	17292	16.5%
1	7066	6.8%
-	6197	5.9%
,	4984	4.8%
	4898	4.7%
(	4029	3.9%
U	4029	3.9%
S	4029	3.9%
Other values (3)	8927	8.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	104526	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	21584	20.6%
9	21491	20.6%
$	17292	16.5%
1	7066	6.8%
-	6197	5.9%
,	4984	4.8%
	4898	4.7%
(	4029	3.9%
U	4029	3.9%
S	4029	3.9%
Other values (3)	8927	8.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	104526	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	21584	20.6%
9	21491	20.6%
$	17292	16.5%
1	7066	6.8%
-	6197	5.9%
,	4984	4.8%
	4898	4.7%
(	4029	3.9%
U	4029	3.9%
S	4029	3.9%
Other values (3)	8927	8.5%

What is the primary tool that you use at work or school to analyze data?
Text

Distinct	3469
Distinct (%)	33.9%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	89
Median length	86
Mean length	75.375318
Min length	25

Characters and Unicode

Total characters	770788
Distinct characters	55
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3030 ?
Unique (%)	29.6%

Sample

1st row	Basic statistical software (Microsoft Excel, Google Sheets, etc.), 0, -1, -1, -1, -1
2nd row	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 0, -1
3rd row	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 1, -1
4th row	Advanced statistical software (SPSS, SAS, etc.), -1, 0, -1, -1, -1
5th row	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 2, -1

Value	Count	Frequency (%)
1	42430	36.7%
etc	9495	8.2%
local	5586	4.8%
development	5586	4.8%
environments	5586	4.8%
rstudio	5586	4.8%
jupyterlab	5586	4.8%
software	3909	3.4%
statistical	2287	2.0%
basic	1652	1.4%
Other values (2157)	27938	24.2%

Most occurring characters

Value	Count	Frequency (%)
	105415	13.7%
,	71742	9.3%
e	62218	8.1%
t	48946	6.4%
1	45968	6.0%
-	42580	5.5%
o	35163	4.6%
a	26808	3.5%
n	25019	3.2%
c	24319	3.2%
Other values (45)	282610	36.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	770788	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	105415	13.7%
,	71742	9.3%
e	62218	8.1%
t	48946	6.4%
1	45968	6.0%
-	42580	5.5%
o	35163	4.6%
a	26808	3.5%
n	25019	3.2%
c	24319	3.2%
Other values (45)	282610	36.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	770788	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	105415	13.7%
,	71742	9.3%
e	62218	8.1%
t	48946	6.4%
1	45968	6.0%
-	42580	5.5%
o	35163	4.6%
a	26808	3.5%
n	25019	3.2%
c	24319	3.2%
Other values (45)	282610	36.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	770788	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	105415	13.7%
,	71742	9.3%
e	62218	8.1%
t	48946	6.4%
1	45968	6.0%
-	42580	5.5%
o	35163	4.6%
a	26808	3.5%
n	25019	3.2%
c	24319	3.2%
Other values (45)	282610	36.7%

How long have you been writing code to analyze data (at work or at school)?
Categorical

Distinct	6
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

3-5 years	2672
1-2 years	2542
< 1 years	1892
5-10 years	1662
10-20 years	955

Length

Max length	11
Median length	9
Mean length	9.3493057
Min length	9

Characters and Unicode

Total characters	95606
Distinct characters	14
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1-2 years
2nd row	1-2 years
3rd row	< 1 years
4th row	20+ years
5th row	3-5 years

Common Values

Value	Count	Frequency (%)
3-5 years	2672	26.1%
1-2 years	2542	24.9%
< 1 years	1892	18.5%
5-10 years	1662	16.3%
10-20 years	955	9.3%
20+ years	503	4.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
years	10226	45.8%
3-5	2672	12.0%
1-2	2542	11.4%
	1892	8.5%
1	1892	8.5%
5-10	1662	7.4%
10-20	955	4.3%
20	503	2.3%

Most occurring characters

Value	Count	Frequency (%)
	12118	12.7%
y	10226	10.7%
e	10226	10.7%
a	10226	10.7%
r	10226	10.7%
s	10226	10.7%
-	7831	8.2%
1	7051	7.4%
5	4334	4.5%
0	4075	4.3%
Other values (4)	9067	9.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	95606	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	12118	12.7%
y	10226	10.7%
e	10226	10.7%
a	10226	10.7%
r	10226	10.7%
s	10226	10.7%
-	7831	8.2%
1	7051	7.4%
5	4334	4.5%
0	4075	4.3%
Other values (4)	9067	9.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	95606	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	12118	12.7%
y	10226	10.7%
e	10226	10.7%
a	10226	10.7%
r	10226	10.7%
s	10226	10.7%
-	7831	8.2%
1	7051	7.4%
5	4334	4.5%
0	4075	4.3%
Other values (4)	9067	9.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	95606	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	12118	12.7%
y	10226	10.7%
e	10226	10.7%
a	10226	10.7%
r	10226	10.7%
s	10226	10.7%
-	7831	8.2%
1	7051	7.4%
5	4334	4.5%
0	4075	4.3%
Other values (4)	9067	9.5%

What programming language would you recommend an aspiring data scientist to learn first?
Categorical

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Python	7879
R	1036
SQL	711
Other	600

Length

Max length	6
Median length	6
Mean length	5.2261881
Min length	1

Characters and Unicode

Total characters	53443
Distinct characters	13
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Python
2nd row	Python
3rd row	Python
4th row	Other
5th row	Python

Common Values

Value	Count	Frequency (%)
Python	7879	77.0%
R	1036	10.1%
SQL	711	7.0%
Other	600	5.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
python	7879	77.0%
r	1036	10.1%
sql	711	7.0%
other	600	5.9%

Most occurring characters

Value	Count	Frequency (%)
t	8479	15.9%
h	8479	15.9%
P	7879	14.7%
y	7879	14.7%
o	7879	14.7%
n	7879	14.7%
R	1036	1.9%
S	711	1.3%
Q	711	1.3%
L	711	1.3%
Other values (3)	1800	3.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	53443	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
t	8479	15.9%
h	8479	15.9%
P	7879	14.7%
y	7879	14.7%
o	7879	14.7%
n	7879	14.7%
R	1036	1.9%
S	711	1.3%
Q	711	1.3%
L	711	1.3%
Other values (3)	1800	3.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	53443	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
t	8479	15.9%
h	8479	15.9%
P	7879	14.7%
y	7879	14.7%
o	7879	14.7%
n	7879	14.7%
R	1036	1.9%
S	711	1.3%
Q	711	1.3%
L	711	1.3%
Other values (3)	1800	3.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	53443	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
t	8479	15.9%
h	8479	15.9%
P	7879	14.7%
y	7879	14.7%
o	7879	14.7%
n	7879	14.7%
R	1036	1.9%
S	711	1.3%
Q	711	1.3%
L	711	1.3%
Other values (3)	1800	3.4%

Have you ever used a TPU (tensor processing unit)?
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Never	8251
Used TPU	1975

Length

Max length	8
Median length	5
Mean length	5.5794054
Min length	5

Characters and Unicode

Total characters	57055
Distinct characters	10
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Never
2nd row	Used TPU
3rd row	Never
4th row	Never
5th row	Used TPU

Common Values

Value	Count	Frequency (%)
Never	8251	80.7%
Used TPU	1975	19.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
never	8251	67.6%
used	1975	16.2%
tpu	1975	16.2%

Most occurring characters

Value	Count	Frequency (%)
e	18477	32.4%
N	8251	14.5%
v	8251	14.5%
r	8251	14.5%
U	3950	6.9%
s	1975	3.5%
d	1975	3.5%
	1975	3.5%
T	1975	3.5%
P	1975	3.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	57055	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	18477	32.4%
N	8251	14.5%
v	8251	14.5%
r	8251	14.5%
U	3950	6.9%
s	1975	3.5%
d	1975	3.5%
	1975	3.5%
T	1975	3.5%
P	1975	3.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	57055	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	18477	32.4%
N	8251	14.5%
v	8251	14.5%
r	8251	14.5%
U	3950	6.9%
s	1975	3.5%
d	1975	3.5%
	1975	3.5%
T	1975	3.5%
P	1975	3.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	57055	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	18477	32.4%
N	8251	14.5%
v	8251	14.5%
r	8251	14.5%
U	3950	6.9%
s	1975	3.5%
d	1975	3.5%
	1975	3.5%
T	1975	3.5%
P	1975	3.5%

For how many years have you used machine learning methods?
Categorical

Distinct	8
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

< 1 years	2965
1-2 years	2641
2-3 years	1526
3-4 years	946
4-5 years	850
Other values (3)	1298

Length

Max length	11
Median length	9
Mean length	9.1414043
Min length	9

Characters and Unicode

Total characters	93480
Distinct characters	15
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1-2 years
2nd row	2-3 years
3rd row	< 1 years
4th row	10-15 years
5th row	2-3 years

Common Values

Value	Count	Frequency (%)
< 1 years	2965	29.0%
1-2 years	2641	25.8%
2-3 years	1526	14.9%
3-4 years	946	9.3%
4-5 years	850	8.3%
5-10 years	808	7.9%
10-15 years	319	3.1%
20+ years	171	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
years	10226	43.7%
	2965	12.7%
1	2965	12.7%
1-2	2641	11.3%
2-3	1526	6.5%
3-4	946	4.0%
4-5	850	3.6%
5-10	808	3.5%
10-15	319	1.4%
20	171	0.7%

Most occurring characters

Value	Count	Frequency (%)
	13191	14.1%
y	10226	10.9%
e	10226	10.9%
a	10226	10.9%
r	10226	10.9%
s	10226	10.9%
-	7090	7.6%
1	7052	7.5%
2	4338	4.6%
<	2965	3.2%
Other values (5)	7714	8.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	93480	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	13191	14.1%
y	10226	10.9%
e	10226	10.9%
a	10226	10.9%
r	10226	10.9%
s	10226	10.9%
-	7090	7.6%
1	7052	7.5%
2	4338	4.6%
<	2965	3.2%
Other values (5)	7714	8.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	93480	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	13191	14.1%
y	10226	10.9%
e	10226	10.9%
a	10226	10.9%
r	10226	10.9%
s	10226	10.9%
-	7090	7.6%
1	7052	7.5%
2	4338	4.6%
<	2965	3.2%
Other values (5)	7714	8.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	93480	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	13191	14.1%
y	10226	10.9%
e	10226	10.9%
a	10226	10.9%
r	10226	10.9%
s	10226	10.9%
-	7090	7.6%
1	7052	7.5%
2	4338	4.6%
<	2965	3.2%
Other values (5)	7714	8.3%

Who/what are your favorite media sources that report on data science topics?
Text

Distinct	902
Distinct (%)	8.8%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	505
Median length	395
Mean length	163.61999
Min length	4

Characters and Unicode

Total characters	1673178
Distinct characters	49
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	304 ?
Unique (%)	3.0%

Sample

1st row	Twitter (data science influencers), Kaggle (forums, blog, social media, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)
2nd row	Podcasts (Chai Time Data Science, Linear Digressions, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc), Slack Communities (ods.ai, kagglenoobs, etc)
3rd row	YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Other
4th row	YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)
5th row	Kaggle (forums, blog, social media, etc), Course Forums (forums.fast.ai, etc), YouTube (Cloud AI Adventures, Siraj Raval, etc), Podcasts (Chai Time Data Science, Linear Digressions, etc), Journal Publications (traditional publications, preprint journals, etc)

Value	Count	Frequency (%)
etc	28445	13.9%
data	10480	5.1%
science	10480	5.1%
forums	9221	4.5%
kaggle	6783	3.3%
blog	6783	3.3%
social	6783	3.3%
media	6783	3.3%
kdnuggets	6549	3.2%
vidhya	6549	3.2%
Other values (36)	105466	51.6%

Most occurring characters

Value	Count	Frequency (%)
	194096	11.6%
e	124951	7.5%
a	117646	7.0%
i	99029	5.9%
t	90897	5.4%
,	90658	5.4%
s	86028	5.1%
c	84481	5.0%
o	78251	4.7%
n	68805	4.1%
Other values (39)	638336	38.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1673178	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	194096	11.6%
e	124951	7.5%
a	117646	7.0%
i	99029	5.9%
t	90897	5.4%
,	90658	5.4%
s	86028	5.1%
c	84481	5.0%
o	78251	4.7%
n	68805	4.1%
Other values (39)	638336	38.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1673178	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	194096	11.6%
e	124951	7.5%
a	117646	7.0%
i	99029	5.9%
t	90897	5.4%
,	90658	5.4%
s	86028	5.1%
c	84481	5.0%
o	78251	4.7%
n	68805	4.1%
Other values (39)	638336	38.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1673178	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	194096	11.6%
e	124951	7.5%
a	117646	7.0%
i	99029	5.9%
t	90897	5.4%
,	90658	5.4%
s	86028	5.1%
c	84481	5.0%
o	78251	4.7%
n	68805	4.1%
Other values (39)	638336	38.2%

On which platforms have you begun or completed data science courses?
Text

Distinct	713
Distinct (%)	7.0%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	169
Median length	148
Mean length	41.716018
Min length	3

Characters and Unicode

Total characters	426588
Distinct characters	35
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	244 ?
Unique (%)	2.4%

Sample

1st row	Coursera, DataCamp, Kaggle Courses (i.e. Kaggle Learn), Udemy
2nd row	Coursera, edX, DataCamp, University Courses (resulting in a university degree)
3rd row	Other
4th row	None
5th row	Udacity, Coursera, edX, Kaggle Courses (i.e. Kaggle Learn), Udemy

Value	Count	Frequency (%)
kaggle	6470	11.5%
courses	5999	10.6%
coursera	5809	10.3%
university	5528	9.8%
learn	3235	5.7%
i.e	3235	5.7%
udemy	3115	5.5%
a	2764	4.9%
degree	2764	4.9%
resulting	2764	4.9%
Other values (10)	14684	26.1%

Most occurring characters

Value	Count	Frequency (%)
e	50570	11.9%
	46141	10.8%
r	33819	7.9%
a	32261	7.6%
s	27655	6.5%
i	24625	5.8%
g	19304	4.5%
n	18367	4.3%
u	17804	4.2%
t	16100	3.8%
Other values (25)	139942	32.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	426588	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	50570	11.9%
	46141	10.8%
r	33819	7.9%
a	32261	7.6%
s	27655	6.5%
i	24625	5.8%
g	19304	4.5%
n	18367	4.3%
u	17804	4.2%
t	16100	3.8%
Other values (25)	139942	32.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	426588	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	50570	11.9%
	46141	10.8%
r	33819	7.9%
a	32261	7.6%
s	27655	6.5%
i	24625	5.8%
g	19304	4.5%
n	18367	4.3%
u	17804	4.2%
t	16100	3.8%
Other values (25)	139942	32.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	426588	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	50570	11.9%
	46141	10.8%
r	33819	7.9%
a	32261	7.6%
s	27655	6.5%
i	24625	5.8%
g	19304	4.5%
n	18367	4.3%
u	17804	4.2%
t	16100	3.8%
Other values (25)	139942	32.8%

Which of the following integrated development environments (IDE's) do you use on a regular basis?
Text

Distinct	752
Distinct (%)	7.4%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	185
Median length	158
Mean length	65.056523
Min length	4

Characters and Unicode

Total characters	665268
Distinct characters	39
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	260 ?
Unique (%)	2.5%

Sample

1st row	Jupyter (JupyterLab, Jupyter Notebooks, etc) , RStudio , PyCharm , MATLAB , Spyder
2nd row	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Visual Studio / Visual Studio Code
3rd row	Jupyter (JupyterLab, Jupyter Notebooks, etc)
4th row	RStudio , Other
5th row	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Spyder , Notepad++ , Sublime Text

Value	Count	Frequency (%)
	21925	23.0%
jupyter	14970	15.7%
notebooks	7485	7.8%
etc	7485	7.8%
jupyterlab	7485	7.8%
visual	6468	6.8%
studio	6468	6.8%
rstudio	3334	3.5%
code	3234	3.4%
pycharm	2999	3.1%
Other values (10)	13678	14.3%

Most occurring characters

Value	Count	Frequency (%)
	129719	19.5%
t	53054	8.0%
e	49514	7.4%
u	40538	6.1%
o	39147	5.9%
,	32269	4.9%
r	28025	4.2%
y	27506	4.1%
p	27004	4.1%
J	22455	3.4%
Other values (29)	216037	32.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	665268	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	129719	19.5%
t	53054	8.0%
e	49514	7.4%
u	40538	6.1%
o	39147	5.9%
,	32269	4.9%
r	28025	4.2%
y	27506	4.1%
p	27004	4.1%
J	22455	3.4%
Other values (29)	216037	32.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	665268	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	129719	19.5%
t	53054	8.0%
e	49514	7.4%
u	40538	6.1%
o	39147	5.9%
,	32269	4.9%
r	28025	4.2%
y	27506	4.1%
p	27004	4.1%
J	22455	3.4%
Other values (29)	216037	32.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	665268	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	129719	19.5%
t	53054	8.0%
e	49514	7.4%
u	40538	6.1%
o	39147	5.9%
,	32269	4.9%
r	28025	4.2%
y	27506	4.1%
p	27004	4.1%
J	22455	3.4%
Other values (29)	216037	32.5%

Which of the following hosted notebook products do you use on a regular basis?
Text

Distinct	228
Distinct (%)	2.2%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	295
Median length	254
Mean length	29.36945
Min length	4

Characters and Unicode

Total characters	300332
Distinct characters	44
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	96 ?
Unique (%)	0.9%

Sample

1st row	None
2nd row	Microsoft Azure Notebooks
3rd row	Google Colab , Google Cloud Notebook Products (AI Platform, Datalab, etc)
4th row	None
5th row	Kaggle Notebooks (Kernels) , Google Colab , Binder / JupyterHub

Value	Count	Frequency (%)
	5411	12.7%
notebooks	5161	12.1%
none	3870	9.1%
google	3770	8.8%
kernels	3225	7.5%
kaggle	3225	7.5%
colab	2981	7.0%
notebook	1427	3.3%
products	1427	3.3%
etc	1427	3.3%
Other values (20)	10816	25.3%

Most occurring characters

Value	Count	Frequency (%)
	47958	16.0%
o	39503	13.2%
e	30393	10.1%
l	15639	5.2%
t	14204	4.7%
b	11584	3.9%
a	11490	3.8%
s	11037	3.7%
g	10858	3.6%
N	10458	3.5%
Other values (34)	97208	32.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	300332	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	47958	16.0%
o	39503	13.2%
e	30393	10.1%
l	15639	5.2%
t	14204	4.7%
b	11584	3.9%
a	11490	3.8%
s	11037	3.7%
g	10858	3.6%
N	10458	3.5%
Other values (34)	97208	32.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	300332	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	47958	16.0%
o	39503	13.2%
e	30393	10.1%
l	15639	5.2%
t	14204	4.7%
b	11584	3.9%
a	11490	3.8%
s	11037	3.7%
g	10858	3.6%
N	10458	3.5%
Other values (34)	97208	32.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	300332	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	47958	16.0%
o	39503	13.2%
e	30393	10.1%
l	15639	5.2%
t	14204	4.7%
b	11584	3.9%
a	11490	3.8%
s	11037	3.7%
g	10858	3.6%
N	10458	3.5%
Other values (34)	97208	32.4%

What programming languages do you use on a regular basis?
Text

Distinct	542
Distinct (%)	5.3%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	70
Median length	58
Mean length	15.099844
Min length	1

Characters and Unicode

Total characters	154411
Distinct characters	29
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	181 ?
Unique (%)	1.8%

Sample

1st row	Python, R, SQL, Java, Javascript, MATLAB
2nd row	Python, R, SQL, Bash
3rd row	Python, SQL
4th row	Python, R
5th row	Python, R, Bash

Value	Count	Frequency (%)
python	9015	33.4%
sql	5218	19.3%
r	3514	13.0%
c	2185	8.1%
bash	1685	6.2%
javascript	1617	6.0%
java	1501	5.6%
other	966	3.6%
matlab	921	3.4%
typescript	331	1.2%

Most occurring characters

Value	Count	Frequency (%)
,	16787	10.9%
	16787	10.9%
t	11929	7.7%
h	11666	7.6%
y	9346	6.1%
o	9075	5.9%
n	9075	5.9%
P	9015	5.8%
a	7921	5.1%
L	6139	4.0%
Other values (19)	46671	30.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	154411	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
,	16787	10.9%
	16787	10.9%
t	11929	7.7%
h	11666	7.6%
y	9346	6.1%
o	9075	5.9%
n	9075	5.9%
P	9015	5.8%
a	7921	5.1%
L	6139	4.0%
Other values (19)	46671	30.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	154411	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
,	16787	10.9%
	16787	10.9%
t	11929	7.7%
h	11666	7.6%
y	9346	6.1%
o	9075	5.9%
n	9075	5.9%
P	9015	5.8%
a	7921	5.1%
L	6139	4.0%
Other values (19)	46671	30.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	154411	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
,	16787	10.9%
	16787	10.9%
t	11929	7.7%
h	11666	7.6%
y	9346	6.1%
o	9075	5.9%
n	9075	5.9%
P	9015	5.8%
a	7921	5.1%
L	6139	4.0%
Other values (19)	46671	30.2%

What data visualization libraries or tools do you use on a regular basis?
Text

Distinct	412
Distinct (%)	4.0%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	141
Median length	123
Mean length	30.946509
Min length	4

Characters and Unicode

Total characters	316459
Distinct characters	38
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	158 ?
Unique (%)	1.5%

Sample

1st row	Matplotlib
2nd row	Ggplot / ggplot2 , Matplotlib , Seaborn
3rd row	Matplotlib , Plotly / Plotly Express , Seaborn
4th row	Ggplot / ggplot2
5th row	Matplotlib , Plotly / Plotly Express , Bokeh , Seaborn

Value	Count	Frequency (%)
	18890	37.4%
matplotlib	7287	14.4%
plotly	4994	9.9%
seaborn	4862	9.6%
ggplot	3177	6.3%
ggplot2	3177	6.3%
express	2497	4.9%
shiny	1079	2.1%
none	915	1.8%
d3.js	903	1.8%
Other values (6)	2722	5.4%

Most occurring characters

Value	Count	Frequency (%)
	70915	22.4%
l	32892	10.4%
t	27347	8.6%
o	26637	8.4%
p	16602	5.2%
,	12758	4.0%
a	12739	4.0%
b	12613	4.0%
e	10864	3.4%
g	9531	3.0%
Other values (28)	83561	26.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	316459	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	70915	22.4%
l	32892	10.4%
t	27347	8.6%
o	26637	8.4%
p	16602	5.2%
,	12758	4.0%
a	12739	4.0%
b	12613	4.0%
e	10864	3.4%
g	9531	3.0%
Other values (28)	83561	26.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	316459	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	70915	22.4%
l	32892	10.4%
t	27347	8.6%
o	26637	8.4%
p	16602	5.2%
,	12758	4.0%
a	12739	4.0%
b	12613	4.0%
e	10864	3.4%
g	9531	3.0%
Other values (28)	83561	26.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	316459	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	70915	22.4%
l	32892	10.4%
t	27347	8.6%
o	26637	8.4%
p	16602	5.2%
,	12758	4.0%
a	12739	4.0%
b	12613	4.0%
e	10864	3.4%
g	9531	3.0%
Other values (28)	83561	26.4%

Which types of specialized hardware do you use on a regular basis?
Categorical

Distinct	14
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

CPUs	3687
CPUs, GPUs	3679
None / I do not know	1683
GPUs	751
CPUs, GPUs, TPUs	248
Other values (9)	178

Length

Max length	23
Median length	20
Mean length	9.1785644
Min length	4

Characters and Unicode

Total characters	93860
Distinct characters	21
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	CPUs, GPUs
2nd row	CPUs, GPUs
3rd row	CPUs, GPUs
4th row	CPUs, GPUs
5th row	CPUs, GPUs

Common Values

Value	Count	Frequency (%)
CPUs	3687	36.1%
CPUs, GPUs	3679	36.0%
None / I do not know	1683	16.5%
GPUs	751	7.3%
CPUs, GPUs, TPUs	248	2.4%
GPUs, TPUs	50	0.5%
Other	40	0.4%
CPUs, TPUs	23	0.2%
CPUs, GPUs, Other	21	0.2%
TPUs	21	0.2%
Other values (4)	23	0.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
cpus	7676	33.4%
gpus	4759	20.7%
none	1683	7.3%
	1683	7.3%
i	1683	7.3%
do	1683	7.3%
not	1683	7.3%
know	1683	7.3%
tpus	348	1.5%
other	84	0.4%

Most occurring characters

Value	Count	Frequency (%)
P	12783	13.6%
U	12783	13.6%
s	12783	13.6%
	12739	13.6%
C	7676	8.2%
o	6732	7.2%
n	5049	5.4%
G	4759	5.1%
,	4324	4.6%
t	1767	1.9%
Other values (11)	12465	13.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	93860	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
P	12783	13.6%
U	12783	13.6%
s	12783	13.6%
	12739	13.6%
C	7676	8.2%
o	6732	7.2%
n	5049	5.4%
G	4759	5.1%
,	4324	4.6%
t	1767	1.9%
Other values (11)	12465	13.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	93860	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
P	12783	13.6%
U	12783	13.6%
s	12783	13.6%
	12739	13.6%
C	7676	8.2%
o	6732	7.2%
n	5049	5.4%
G	4759	5.1%
,	4324	4.6%
t	1767	1.9%
Other values (11)	12465	13.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	93860	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
P	12783	13.6%
U	12783	13.6%
s	12783	13.6%
	12739	13.6%
C	7676	8.2%
o	6732	7.2%
n	5049	5.4%
G	4759	5.1%
,	4324	4.6%
t	1767	1.9%
Other values (11)	12465	13.3%

Which of the following ML algorithms do you use on a regular basis?
Text

Distinct	630
Distinct (%)	6.2%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	336
Median length	288
Mean length	103.96548
Min length	4

Characters and Unicode

Total characters	1063151
Distinct characters	43
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	230 ?
Unique (%)	2.2%

Sample

1st row	Linear or Logistic Regression
2nd row	Linear or Logistic Regression, Convolutional Neural Networks
3rd row	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc)
4th row	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc), Bayesian Approaches, Convolutional Neural Networks, Generative Adversarial Networks, Recurrent Neural Networks
5th row	Linear or Logistic Regression, Dense Neural Networks (MLPs, etc), Convolutional Neural Networks, Recurrent Neural Networks

Value	Count	Frequency (%)
or	13843	10.4%
networks	10146	7.7%
neural	8735	6.6%
linear	7454	5.6%
logistic	7454	5.6%
regression	7454	5.6%
etc	7434	5.6%
decision	6389	4.8%
trees	6389	4.8%
random	6389	4.8%
Other values (20)	50826	38.4%

Most occurring characters

Value	Count	Frequency (%)
	122287	11.5%
e	103625	9.7%
o	93270	8.8%
s	83530	7.9%
r	78496	7.4%
i	68651	6.5%
n	58971	5.5%
t	57545	5.4%
a	47648	4.5%
,	35206	3.3%
Other values (33)	313922	29.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1063151	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	122287	11.5%
e	103625	9.7%
o	93270	8.8%
s	83530	7.9%
r	78496	7.4%
i	68651	6.5%
n	58971	5.5%
t	57545	5.4%
a	47648	4.5%
,	35206	3.3%
Other values (33)	313922	29.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1063151	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	122287	11.5%
e	103625	9.7%
o	93270	8.8%
s	83530	7.9%
r	78496	7.4%
i	68651	6.5%
n	58971	5.5%
t	57545	5.4%
a	47648	4.5%
,	35206	3.3%
Other values (33)	313922	29.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1063151	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	122287	11.5%
e	103625	9.7%
o	93270	8.8%
s	83530	7.9%
r	78496	7.4%
i	68651	6.5%
n	58971	5.5%
t	57545	5.4%
a	47648	4.5%
,	35206	3.3%
Other values (33)	313922	29.5%

Which categories of ML tools do you use on a regular basis?
Text

Distinct	92
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	374
Median length	4
Mean length	46.464209
Min length	4

Characters and Unicode

Total characters	475143
Distinct characters	41
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	17 ?
Unique (%)	0.2%

Sample

1st row	None
2nd row	Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)
3rd row	None
4th row	Automated model selection (e.g. auto-sklearn, xcessiv), Automated hyperparameter tuning (e.g. hyperopt, ray.tune), Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)
5th row	Automated data augmentation (e.g. imgaug, albumentations), Automated feature engineering/selection (e.g. tpot, boruta_py), Automated model selection (e.g. auto-sklearn, xcessiv), Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)

Value	Count	Frequency (%)
e.g	7591	13.4%
automated	6645	11.8%
none	5707	10.1%
model	2625	4.6%
selection	2257	4.0%
auto-sklearn	2257	4.0%
xcessiv	2257	4.0%
tuning	1463	2.6%
ray.tune	1463	2.6%
hyperopt	1463	2.6%
Other values (24)	22805	40.3%

Most occurring characters

Value	Count	Frequency (%)
e	56882	12.0%
	46307	9.7%
t	40565	8.5%
o	32965	6.9%
a	29760	6.3%
n	27098	5.7%
u	21484	4.5%
i	17800	3.7%
r	16781	3.5%
.	16645	3.5%
Other values (31)	168856	35.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	475143	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	56882	12.0%
	46307	9.7%
t	40565	8.5%
o	32965	6.9%
a	29760	6.3%
n	27098	5.7%
u	21484	4.5%
i	17800	3.7%
r	16781	3.5%
.	16645	3.5%
Other values (31)	168856	35.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	475143	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	56882	12.0%
	46307	9.7%
t	40565	8.5%
o	32965	6.9%
a	29760	6.3%
n	27098	5.7%
u	21484	4.5%
i	17800	3.7%
r	16781	3.5%
.	16645	3.5%
Other values (31)	168856	35.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	475143	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	56882	12.0%
	46307	9.7%
t	40565	8.5%
o	32965	6.9%
a	29760	6.3%
n	27098	5.7%
u	21484	4.5%
i	17800	3.7%
r	16781	3.5%
.	16645	3.5%
Other values (31)	168856	35.5%

Which of the following machine learning frameworks do you use on a regular basis?
Text

Distinct	554
Distinct (%)	5.4%
Missing	0
Missing (%)	0.0%
Memory size	80.0 KiB

Length

Max length	129
Median length	107
Mean length	36.544299
Min length	4

Characters and Unicode

Total characters	373702
Distinct characters	37
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	194 ?
Unique (%)	1.9%

Sample

1st row	None
2nd row	Scikit-learn , TensorFlow , Keras , RandomForest
3rd row	Scikit-learn , RandomForest, Xgboost , LightGBM
4th row	Scikit-learn , TensorFlow , Keras , RandomForest, Xgboost , Caret
5th row	Scikit-learn , TensorFlow , Keras , PyTorch

Value	Count	Frequency (%)
	17585	35.9%
scikit-learn	6883	14.1%
keras	4265	8.7%
tensorflow	4233	8.6%
randomforest	3457	7.1%
xgboost	3367	6.9%
pytorch	2517	5.1%
lightgbm	1734	3.5%
none	1302	2.7%
caret	984	2.0%
Other values (4)	2616	5.3%

Most occurring characters

Value	Count	Frequency (%)
	86727	23.2%
o	25933	6.9%
r	23427	6.3%
e	21408	5.7%
,	20328	5.4%
a	17841	4.8%
t	17433	4.7%
i	17028	4.6%
s	16046	4.3%
n	15875	4.2%
Other values (27)	111656	29.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	373702	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	86727	23.2%
o	25933	6.9%
r	23427	6.3%
e	21408	5.7%
,	20328	5.4%
a	17841	4.8%
t	17433	4.7%
i	17028	4.6%
s	16046	4.3%
n	15875	4.2%
Other values (27)	111656	29.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	373702	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	86727	23.2%
o	25933	6.9%
r	23427	6.3%
e	21408	5.7%
,	20328	5.4%
a	17841	4.8%
t	17433	4.7%
i	17028	4.6%
s	16046	4.3%
n	15875	4.2%
Other values (27)	111656	29.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	373702	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	86727	23.2%
o	25933	6.9%
r	23427	6.3%
e	21408	5.7%
,	20328	5.4%
a	17841	4.8%
t	17433	4.7%
i	17028	4.6%
s	16046	4.3%
n	15875	4.2%
Other values (27)	111656	29.9%

Correlations

	Approximately how many individuals are responsible for data science workloads at your place of business?	Approximately how much money have you spent on machine learning and/or cloud computing products at your work in the past 5 years?	Does your current employer incorporate machine learning methods into their business?	For how many years have you used machine learning methods?	Have you ever used a TPU (tensor processing unit)?	How long have you been writing code to analyze data (at work or at school)?	Select the title most similar to your current role (or most recent title if retired)	What is the highest level of formal education that you have attained or plan to attain within the next 2 years?	What is the size of the company where you are employed?	What is your age (# years)?	What is your current yearly compensation (approximate $USD)?	What is your gender?	What programming language would you recommend an aspiring data scientist to learn first?	Which types of specialized hardware do you use on a regular basis?
Approximately how many individuals are responsible for data science workloads at your place of business?	1.000	0.150	0.244	0.112	0.067	0.123	0.112	0.054	0.301	0.033	0.104	0.023	0.030	0.036
Approximately how much money have you spent on machine learning and/or cloud computing products at your work in the past 5 years?	0.150	1.000	0.167	0.157	0.113	0.144	0.087	0.045	0.102	0.096	0.196	0.049	0.032	0.088
Does your current employer incorporate machine learning methods into their business?	0.244	0.167	1.000	0.202	0.098	0.143	0.162	0.062	0.120	0.050	0.137	0.032	0.030	0.098
For how many years have you used machine learning methods?	0.112	0.157	0.202	1.000	0.133	0.464	0.154	0.148	0.036	0.160	0.165	0.065	0.063	0.113
Have you ever used a TPU (tensor processing unit)?	0.067	0.113	0.098	0.133	1.000	0.074	0.087	0.040	0.051	0.082	0.050	0.037	0.069	0.411
How long have you been writing code to analyze data (at work or at school)?	0.123	0.144	0.143	0.464	0.074	1.000	0.145	0.149	0.058	0.282	0.228	0.050	0.068	0.077
Select the title most similar to your current role (or most recent title if retired)	0.112	0.087	0.162	0.154	0.087	0.145	1.000	0.180	0.052	0.084	0.068	0.086	0.135	0.085
What is the highest level of formal education that you have attained or plan to attain within the next 2 years?	0.054	0.045	0.062	0.148	0.040	0.149	0.180	1.000	0.060	0.152	0.086	0.052	0.054	0.037
What is the size of the company where you are employed?	0.301	0.102	0.120	0.036	0.051	0.058	0.052	0.060	1.000	0.071	0.137	0.030	0.041	0.040
What is your age (# years)?	0.033	0.096	0.050	0.160	0.082	0.282	0.084	0.152	0.071	1.000	0.148	0.064	0.087	0.034
What is your current yearly compensation (approximate $USD)?	0.104	0.196	0.137	0.165	0.050	0.228	0.068	0.086	0.137	0.148	1.000	0.077	0.057	0.044
What is your gender?	0.023	0.049	0.032	0.065	0.037	0.050	0.086	0.052	0.030	0.064	0.077	1.000	0.040	0.163
What programming language would you recommend an aspiring data scientist to learn first?	0.030	0.032	0.030	0.063	0.069	0.068	0.135	0.054	0.041	0.087	0.057	0.040	1.000	0.111
Which types of specialized hardware do you use on a regular basis?	0.036	0.088	0.098	0.113	0.411	0.077	0.085	0.037	0.040	0.034	0.044	0.163	0.111	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

	What is your age (# years)?	What is your gender?	In which country do you currently reside?	What is the highest level of formal education that you have attained or plan to attain within the next 2 years?	Select the title most similar to your current role (or most recent title if retired)	What is the size of the company where you are employed?	Approximately how many individuals are responsible for data science workloads at your place of business?	Does your current employer incorporate machine learning methods into their business?	What is your current yearly compensation (approximate $USD)?	Approximately how much money have you spent on machine learning and/or cloud computing products at your work in the past 5 years?	What is the primary tool that you use at work or school to analyze data?	How long have you been writing code to analyze data (at work or at school)?	What programming language would you recommend an aspiring data scientist to learn first?	Have you ever used a TPU (tensor processing unit)?	For how many years have you used machine learning methods?	Who/what are your favorite media sources that report on data science topics?	On which platforms have you begun or completed data science courses?	Which of the following integrated development environments (IDE's) do you use on a regular basis?	Which of the following hosted notebook products do you use on a regular basis?	What programming languages do you use on a regular basis?	What data visualization libraries or tools do you use on a regular basis?	Which types of specialized hardware do you use on a regular basis?	Which of the following ML algorithms do you use on a regular basis?	Which categories of ML tools do you use on a regular basis?	Which of the following machine learning frameworks do you use on a regular basis?
0	22-24	Male	France	Master’s degree	Software Engineer	1000-9,999 employees	0	I do not know	30,000-39,999	$0 (USD)	Basic statistical software (Microsoft Excel, Google Sheets, etc.), 0, -1, -1, -1, -1	1-2 years	Python	Never	1-2 years	Twitter (data science influencers), Kaggle (forums, blog, social media, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)	Coursera, DataCamp, Kaggle Courses (i.e. Kaggle Learn), Udemy	Jupyter (JupyterLab, Jupyter Notebooks, etc) , RStudio , PyCharm , MATLAB , Spyder	None	Python, R, SQL, Java, Javascript, MATLAB	Matplotlib	CPUs, GPUs	Linear or Logistic Regression	None	None
1	40-44	Male	Australia	Master’s degree	Other	> 10,000 employees	20+	I do not know	250,000-299,999	$10,000-$99,999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 0, -1	1-2 years	Python	Used TPU	2-3 years	Podcasts (Chai Time Data Science, Linear Digressions, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc), Slack Communities (ods.ai, kagglenoobs, etc)	Coursera, edX, DataCamp, University Courses (resulting in a university degree)	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Visual Studio / Visual Studio Code	Microsoft Azure Notebooks	Python, R, SQL, Bash	Ggplot / ggplot2 , Matplotlib , Seaborn	CPUs, GPUs	Linear or Logistic Regression, Convolutional Neural Networks	Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)	Scikit-learn , TensorFlow , Keras , RandomForest
2	22-24	Male	India	Bachelor’s degree	Other	0-49 employees	0	No (we do not use ML methods)	4,000-4,999	$0 (USD)	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 1, -1	< 1 years	Python	Never	< 1 years	YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Other	Other	Jupyter (JupyterLab, Jupyter Notebooks, etc)	Google Colab , Google Cloud Notebook Products (AI Platform, Datalab, etc)	Python, SQL	Matplotlib , Plotly / Plotly Express , Seaborn	CPUs, GPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc)	None	Scikit-learn , RandomForest, Xgboost , LightGBM
3	50-54	Male	France	Master’s degree	Data Scientist	0-49 employees	3-4	We have well established ML methods (i.e., models in production for more than 2 years)	60,000-69,999	$10,000-$99,999	Advanced statistical software (SPSS, SAS, etc.), -1, 0, -1, -1, -1	20+ years	Other	Never	10-15 years	YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)	None	RStudio , Other	None	Python, R	Ggplot / ggplot2	CPUs, GPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc), Bayesian Approaches, Convolutional Neural Networks, Generative Adversarial Networks, Recurrent Neural Networks	Automated model selection (e.g. auto-sklearn, xcessiv), Automated hyperparameter tuning (e.g. hyperopt, ray.tune), Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)	Scikit-learn , TensorFlow , Keras , RandomForest, Xgboost , Caret
4	22-24	Male	India	Master’s degree	Data Scientist	50-249 employees	20+	We are exploring ML methods (and may one day put a model into production)	10,000-14,999	$100-$999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 2, -1	3-5 years	Python	Used TPU	2-3 years	Kaggle (forums, blog, social media, etc), Course Forums (forums.fast.ai, etc), YouTube (Cloud AI Adventures, Siraj Raval, etc), Podcasts (Chai Time Data Science, Linear Digressions, etc), Journal Publications (traditional publications, preprint journals, etc)	Udacity, Coursera, edX, Kaggle Courses (i.e. Kaggle Learn), Udemy	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Spyder , Notepad++ , Sublime Text	Kaggle Notebooks (Kernels) , Google Colab , Binder / JupyterHub	Python, R, Bash	Matplotlib , Plotly / Plotly Express , Bokeh , Seaborn	CPUs, GPUs	Linear or Logistic Regression, Dense Neural Networks (MLPs, etc), Convolutional Neural Networks, Recurrent Neural Networks	Automated data augmentation (e.g. imgaug, albumentations), Automated feature engineering/selection (e.g. tpot, boruta_py), Automated model selection (e.g. auto-sklearn, xcessiv), Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)	Scikit-learn , TensorFlow , Keras , PyTorch
5	22-24	Female	United States of America	Bachelor’s degree	Data Scientist	> 10,000 employees	20+	We recently started using ML methods (i.e., models in production for less than 2 years)	80,000-89,999	$0 (USD)	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 3, -1	3-5 years	Python	Used TPU	3-4 years	Hacker News (https://news.ycombinator.com/), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)	Udemy, University Courses (resulting in a university degree)	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Spyder	Microsoft Azure Notebooks , AWS Notebook Products (EMR Notebooks, Sagemaker Notebooks, etc)	Python	Matplotlib , Plotly / Plotly Express	CPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Convolutional Neural Networks	None	Scikit-learn , TensorFlow , Keras , Spark MLib
6	55-59	Male	Netherlands	Master’s degree	Other	0-49 employees	1-2	We are exploring ML methods (and may one day put a model into production)	$0-999	$100-$999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 5, -1	5-10 years	Python	Never	< 1 years	Kaggle (forums, blog, social media, etc), Course Forums (forums.fast.ai, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)	Coursera	Jupyter (JupyterLab, Jupyter Notebooks, etc)	None	Python, SQL	Matplotlib , D3.js , Seaborn	CPUs	Linear or Logistic Regression, Bayesian Approaches, Generative Adversarial Networks	None	Scikit-learn , PyTorch
7	30-34	Male	Germany	Master’s degree	Statistician	0-49 employees	5-9	We recently started using ML methods (i.e., models in production for less than 2 years)	2,000-2,999	$1000-$9,999	Basic statistical software (Microsoft Excel, Google Sheets, etc.), 2, -1, -1, -1, -1	5-10 years	R	Used TPU	4-5 years	Podcasts (Chai Time Data Science, Linear Digressions, etc)	Coursera	Jupyter (JupyterLab, Jupyter Notebooks, etc)	Code Ocean	R	Matplotlib	CPUs	Bayesian Approaches	Automated data augmentation (e.g. imgaug, albumentations)	Scikit-learn
8	30-34	Male	Germany	Bachelor’s degree	Data Scientist	50-249 employees	5-9	We recently started using ML methods (i.e., models in production for less than 2 years)	70,000-79,999	$1000-$9,999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 6, -1	5-10 years	R	Never	4-5 years	None	edX	Jupyter (JupyterLab, Jupyter Notebooks, etc) , RStudio	None	Python, R	Ggplot / ggplot2	CPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc), Bayesian Approaches, Dense Neural Networks (MLPs, etc)	None	Keras , Caret
9	30-34	Male	United States of America	Master’s degree	Product/Project Manager	> 10,000 employees	20+	I do not know	90,000-99,999	$0 (USD)	Basic statistical software (Microsoft Excel, Google Sheets, etc.), 1, -1, -1, -1, -1	3-5 years	Python	Never	2-3 years	Hacker News (https://news.ycombinator.com/), Reddit (r/machinelearning, r/datascience, etc), Kaggle (forums, blog, social media, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)	Udacity, Coursera, DataQuest, Kaggle Courses (i.e. Kaggle Learn), Fast.ai, Udemy, University Courses (resulting in a university degree)	Jupyter (JupyterLab, Jupyter Notebooks, etc) , PyCharm , Atom , Notepad++ , Sublime Text	Kaggle Notebooks (Kernels) , Google Colab , Google Cloud Notebook Products (AI Platform, Datalab, etc) , Code Ocean	Python	Matplotlib , Plotly / Plotly Express , Seaborn	None / I do not know	Linear or Logistic Regression, Decision Trees or Random Forests, Bayesian Approaches	None	Scikit-learn , RandomForest

	What is your age (# years)?	What is your gender?	In which country do you currently reside?	What is the highest level of formal education that you have attained or plan to attain within the next 2 years?	Select the title most similar to your current role (or most recent title if retired)	What is the size of the company where you are employed?	Approximately how many individuals are responsible for data science workloads at your place of business?	Does your current employer incorporate machine learning methods into their business?	What is your current yearly compensation (approximate $USD)?	Approximately how much money have you spent on machine learning and/or cloud computing products at your work in the past 5 years?	What is the primary tool that you use at work or school to analyze data?	How long have you been writing code to analyze data (at work or at school)?	What programming language would you recommend an aspiring data scientist to learn first?	Have you ever used a TPU (tensor processing unit)?	For how many years have you used machine learning methods?	Who/what are your favorite media sources that report on data science topics?	On which platforms have you begun or completed data science courses?	Which of the following integrated development environments (IDE's) do you use on a regular basis?	Which of the following hosted notebook products do you use on a regular basis?	What programming languages do you use on a regular basis?	What data visualization libraries or tools do you use on a regular basis?	Which types of specialized hardware do you use on a regular basis?	Which of the following ML algorithms do you use on a regular basis?	Which categories of ML tools do you use on a regular basis?	Which of the following machine learning frameworks do you use on a regular basis?
10216	50-54	Male	France	Some college/university study without earning a bachelor’s degree	Data Scientist	0-49 employees	3-4	We use ML methods for generating insights (but do not put working models into production)	100,000-124,999	$10,000-$99,999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 125, -1	5-10 years	Python	Used TPU	4-5 years	Twitter (data science influencers), Hacker News (https://news.ycombinator.com/), Reddit (r/machinelearning, r/datascience, etc), Kaggle (forums, blog, social media, etc), Course Forums (forums.fast.ai, etc), YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc), Slack Communities (ods.ai, kagglenoobs, etc)	Udacity, Coursera, edX, Kaggle Courses (i.e. Kaggle Learn), University Courses (resulting in a university degree), Other	Jupyter (JupyterLab, Jupyter Notebooks, etc) , RStudio , PyCharm , Visual Studio / Visual Studio Code	Kaggle Notebooks (Kernels) , Google Colab , Microsoft Azure Notebooks , Binder / JupyterHub	Python, SQL, C++	Matplotlib , Shiny , Plotly / Plotly Express	TPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc), Bayesian Approaches, Evolutionary Approaches, Dense Neural Networks (MLPs, etc), Convolutional Neural Networks, Generative Adversarial Networks, Recurrent Neural Networks, Transformer Networks (BERT, gpt-2, etc)	Automated data augmentation (e.g. imgaug, albumentations), Automated feature engineering/selection (e.g. tpot, boruta_py), Automated model selection (e.g. auto-sklearn, xcessiv)	Scikit-learn , TensorFlow , PyTorch , Spark MLib
10217	25-29	Male	Nigeria	Doctoral degree	Data Scientist	250-999 employees	1-2	We are exploring ML methods (and may one day put a model into production)	1,000-1,999	$100-$999	Business intelligence software (Salesforce, Tableau, Spotfire, etc.), -1, -1, 337, -1, -1	< 1 years	Python	Used TPU	< 1 years	Reddit (r/machinelearning, r/datascience, etc), YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)	Udacity, edX, Udemy	Visual Studio / Visual Studio Code	Microsoft Azure Notebooks	Python, R, SQL	Ggplot / ggplot2	GPUs	Bayesian Approaches	Automated data augmentation (e.g. imgaug, albumentations), Automated hyperparameter tuning (e.g. hyperopt, ray.tune), Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)	Fast.ai
10218	18-21	Male	Nigeria	Bachelor’s degree	Data Analyst	250-999 employees	5-9	I do not know	5,000-7,499	$1000-$9,999	Advanced statistical software (SPSS, SAS, etc.), -1, 253, -1, -1, -1	< 1 years	R	Never	< 1 years	Hacker News (https://news.ycombinator.com/), Kaggle (forums, blog, social media, etc)	DataCamp	RStudio	None	Python, R	Ggplot / ggplot2	CPUs	Linear or Logistic Regression	Automated feature engineering/selection (e.g. tpot, boruta_py)	None
10219	35-39	Male	Saudi Arabia	Master’s degree	Data Scientist	> 10,000 employees	5-9	We have well established ML methods (i.e., models in production for more than 2 years)	100,000-124,999	$10,000-$99,999	Other, -1, -1, -1, -1, -1	5-10 years	R	Never	10-15 years	YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Slack Communities (ods.ai, kagglenoobs, etc)	Coursera, DataCamp	RStudio	None	R, SQL	Ggplot / ggplot2 , Shiny , Plotly / Plotly Express , Leaflet / Folium	CPUs	Gradient Boosting Machines (xgboost, lightgbm, etc)	None	Xgboost , Caret
10220	25-29	Male	Viet Nam	Master’s degree	Data Analyst	50-249 employees	1-2	We are exploring ML methods (and may one day put a model into production)	$0-999	$1-$99	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 50, -1	1-2 years	Python	Never	1-2 years	Kaggle (forums, blog, social media, etc), Course Forums (forums.fast.ai, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)	Udacity, Coursera, Kaggle Courses (i.e. Kaggle Learn), Fast.ai, Udemy, LinkedIn Learning	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Sublime Text	Kaggle Notebooks (Kernels) , Google Colab	Python	Matplotlib , Seaborn	CPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc), Bayesian Approaches	Automated model selection (e.g. auto-sklearn, xcessiv), Automated hyperparameter tuning (e.g. hyperopt, ray.tune)	Scikit-learn , Xgboost
10221	25-29	Male	India	Master’s degree	Data Scientist	0-49 employees	1-2	We recently started using ML methods (i.e., models in production for less than 2 years)	1,000-1,999	$100-$999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 2838, -1	3-5 years	Python	Never	2-3 years	Hacker News (https://news.ycombinator.com/), Kaggle (forums, blog, social media, etc), YouTube (Cloud AI Adventures, Siraj Raval, etc), Slack Communities (ods.ai, kagglenoobs, etc)	Kaggle Courses (i.e. Kaggle Learn), LinkedIn Learning	Jupyter (JupyterLab, Jupyter Notebooks, etc) , PyCharm , MATLAB , Notepad++	Google Cloud Notebook Products (AI Platform, Datalab, etc) , AWS Notebook Products (EMR Notebooks, Sagemaker Notebooks, etc)	Python, MATLAB	Matplotlib	CPUs, GPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Convolutional Neural Networks	Automated data augmentation (e.g. imgaug, albumentations)	Scikit-learn , TensorFlow , PyTorch , Spark MLib
10222	22-24	Female	Other	Bachelor’s degree	Other	50-249 employees	1-2	We are exploring ML methods (and may one day put a model into production)	5,000-7,499	$100-$999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 0, -1	1-2 years	Python	Never	1-2 years	Other	Udacity, Coursera, edX, Kaggle Courses (i.e. Kaggle Learn), University Courses (resulting in a university degree), Other	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Atom , Visual Studio / Visual Studio Code , Spyder	Google Colab	Python	Matplotlib , Seaborn	CPUs, GPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Dense Neural Networks (MLPs, etc), Convolutional Neural Networks	Automated hyperparameter tuning (e.g. hyperopt, ray.tune)	Scikit-learn , TensorFlow , PyTorch
10223	25-29	Male	China	I prefer not to answer	Data Engineer	250-999 employees	5-9	We recently started using ML methods (i.e., models in production for less than 2 years)	20,000-24,999	$100-$999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 12, -1	1-2 years	Python	Used TPU	1-2 years	Other	Kaggle Courses (i.e. Kaggle Learn)	Jupyter (JupyterLab, Jupyter Notebooks, etc) , PyCharm	Google Colab	Python	Seaborn	GPUs	Dense Neural Networks (MLPs, etc), Recurrent Neural Networks	None	Scikit-learn , TensorFlow , Keras
10224	25-29	Male	Australia	Bachelor’s degree	Other	1000-9,999 employees	5-9	No (we do not use ML methods)	60,000-69,999	$10,000-$99,999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 14, -1	3-5 years	Python	Never	1-2 years	Hacker News (https://news.ycombinator.com/), Reddit (r/machinelearning, r/datascience, etc), Kaggle (forums, blog, social media, etc), Podcasts (Chai Time Data Science, Linear Digressions, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)	Coursera, edX, Fast.ai, Udemy	Jupyter (JupyterLab, Jupyter Notebooks, etc) , MATLAB , Visual Studio / Visual Studio Code	None	Python, SQL, MATLAB	Matplotlib , Plotly / Plotly Express , Bokeh , Seaborn	CPUs, GPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc), Bayesian Approaches	None	Scikit-learn , TensorFlow , PyTorch
10225	50-54	Male	France	Bachelor’s degree	Software Engineer	> 10,000 employees	20+	We have well established ML methods (i.e., models in production for more than 2 years)	60,000-69,999	$0 (USD)	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 25, -1	3-5 years	Python	Never	4-5 years	Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)	Coursera, edX, Udemy	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Visual Studio / Visual Studio Code	IBM Watson Studio	Python, SQL, Java, Bash	Matplotlib	CPUs	Linear or Logistic Regression, Decision Trees or Random Forests	Automated model selection (e.g. auto-sklearn, xcessiv), Automated hyperparameter tuning (e.g. hyperopt, ray.tune)	Scikit-learn , Spark MLib

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